Author

Sebastian Zeki

Published

July 23, 2018

Modified

March 23, 2024

There are many occasions when a column of data needs to be created from an already existing column for ease of data manipulation. For example, perhaps you have a body of text as a pathology report and you want to extract all the reports where the diagnosis is ‘dysplasia’.

You could just subset the data using grepl so that you only get the reports that mention this word…but what if the data needs to be cleaned prior to subsetting like excluding reports where the diagnosis is normal but the phrase ‘No evidence of dysplasia’ is present. Or perhaps there are other manipulations needed prior to subsetting.

This is where data accordionisation is useful. This simply means the creation of data from (usually) a column into another column in the same dataframe.

The neatest way to do this is with the mutate function from the {dplyr} package which is devoted to data cleaning. There are also other ways which I will demonstrate at the end.

The input data here will be an endoscopy data set:

Age <- sample(1:100, 130, replace = TRUE)
Dx <- sample(c("NDBE", "LGD", "HGD", "IMC"), 130, replace = TRUE)
TimeOfEndoscopy <- sample(1:60, 130, replace = TRUE)

library(dplyr)

EMRdf <- data.frame(Age, Dx, TimeOfEndoscopy, stringsAsFactors = F)

Perhaps you need to calculate the number of hours spent doing each endoscopy rather than the number of minutes

EMRdftbb <- EMRdf %>% mutate(TimeOfEndoscopy / 60)

# install.packages("knitr")
library(knitr)
library(kableExtra)

# Just show the top 20 results

kable(head(EMRdftbb, 20))
Age Dx TimeOfEndoscopy TimeOfEndoscopy/60
69 HGD 9 0.1500000
51 IMC 36 0.6000000
55 IMC 32 0.5333333
91 HGD 13 0.2166667
6 NDBE 59 0.9833333
69 HGD 9 0.1500000
44 HGD 5 0.0833333
98 LGD 32 0.5333333
66 LGD 34 0.5666667
58 LGD 35 0.5833333
48 IMC 17 0.2833333
56 IMC 30 0.5000000
16 IMC 33 0.5500000
73 LGD 13 0.2166667
23 IMC 17 0.2833333
75 HGD 60 1.0000000
19 IMC 16 0.2666667
70 NDBE 8 0.1333333
21 IMC 17 0.2833333
91 NDBE 30 0.5000000

That is useful but what if you want to classify the amount of time spent doing each endoscopy as follows: <0.4 hours is too little time and >0.4 hours is too long.

Using ifelse() with mutate for conditional accordionisation.

For this we would use ifelse(). However this can be combined with mutate() so that the result gets put in another column as follows

EMRdf2 <- EMRdf %>%
  mutate(TimeInHours = TimeOfEndoscopy / 60) %>%
  mutate(TimeClassification = ifelse(TimeInHours > 0.4, "Too Long", "Too Short"))

# Just show the top 20 results

kable(head(EMRdf2, 20))
Age Dx TimeOfEndoscopy TimeInHours TimeClassification
69 HGD 9 0.1500000 Too Short
51 IMC 36 0.6000000 Too Long
55 IMC 32 0.5333333 Too Long
91 HGD 13 0.2166667 Too Short
6 NDBE 59 0.9833333 Too Long
69 HGD 9 0.1500000 Too Short
44 HGD 5 0.0833333 Too Short
98 LGD 32 0.5333333 Too Long
66 LGD 34 0.5666667 Too Long
58 LGD 35 0.5833333 Too Long
48 IMC 17 0.2833333 Too Short
56 IMC 30 0.5000000 Too Long
16 IMC 33 0.5500000 Too Long
73 LGD 13 0.2166667 Too Short
23 IMC 17 0.2833333 Too Short
75 HGD 60 1.0000000 Too Long
19 IMC 16 0.2666667 Too Short
70 NDBE 8 0.1333333 Too Short
21 IMC 17 0.2833333 Too Short
91 NDBE 30 0.5000000 Too Long

Note how we can chain the mutate() function together.

Using multiple ifelse()

What if we want to get more complex and put several classifiers in? We just use more ifelse’s:

EMRdf2 <- EMRdf %>%
  mutate(TimeInHours = TimeOfEndoscopy / 60) %>%
  mutate(TimeClassification = ifelse(TimeInHours > 0.8, "Too Long", ifelse(TimeInHours < 0.5, "Too Short", ifelse(TimeInHours >= 0.5 & TimeInHours <= 0.8, "Just Right", "N"))))

# Just show the top 20 results

kable(head(EMRdf2, 20))
Age Dx TimeOfEndoscopy TimeInHours TimeClassification
69 HGD 9 0.1500000 Too Short
51 IMC 36 0.6000000 Just Right
55 IMC 32 0.5333333 Just Right
91 HGD 13 0.2166667 Too Short
6 NDBE 59 0.9833333 Too Long
69 HGD 9 0.1500000 Too Short
44 HGD 5 0.0833333 Too Short
98 LGD 32 0.5333333 Just Right
66 LGD 34 0.5666667 Just Right
58 LGD 35 0.5833333 Just Right
48 IMC 17 0.2833333 Too Short
56 IMC 30 0.5000000 Just Right
16 IMC 33 0.5500000 Just Right
73 LGD 13 0.2166667 Too Short
23 IMC 17 0.2833333 Too Short
75 HGD 60 1.0000000 Too Long
19 IMC 16 0.2666667 Too Short
70 NDBE 8 0.1333333 Too Short
21 IMC 17 0.2833333 Too Short
91 NDBE 30 0.5000000 Just Right

Using multiple ifelse() with grepl() or string_extract

Of course we need to extract information from text as well as numeric data. We can do this using grepl() or string_extract() from the library(stringr).

Let’s say we want to extract all the samples that had IMC. We don’t want to subset the data, just extract IMC into a column that says IMC and the rest say ’Non-IMC’

Using the dataset above:

library(stringr)

EMRdf$MyIMC_Column <- str_extract(EMRdf$Dx, "IMC")

# to fill the NA's we would do:EMRdf$MyIMC_Column<-ifelse(grepl("IMC",EMRdf$Dx),"IMC","NoIMC")

# Another way to do this (really should be for more complex examples when you want to extract the entire contents of the cell that has the match)

EMRdf$MyIMC_Column <- ifelse(grepl("IMC", EMRdf$Dx), str_extract(EMRdf$Dx, "IMC"), "NoIMC")

So data can be usefully created from data for further analysis.

Hopefully this way of extrapolating data and especially using conditional expressions to categorise data according to some rules is a helpful way to get more out of your data.

Please follow @gastroDS on twitter

This article originally appeared on https://sebastiz.github.io/gastrodatascience/ and has been edited to render in Quarto and had NHS-R styles applied.

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For attribution, please cite this work as:
Zeki, Sebastian. 2018. “How to Extrapolate Data from Data.” July 23, 2018. https://nhs-r-community.github.io/nhs-r-community//blog/how-to-extrapolate-data-from-data.html.